TY - GEN

T1 - Distributed kernel regression

T2 - 2006 IEEE Information Theory Workshop, ITW 2006

AU - Predd, J. B.

AU - Kulkarni, S. R.

AU - Poor, H. V.

N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

PY - 2006

Y1 - 2006

N2 - This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.

AB - This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.

UR - http://www.scopus.com/inward/record.url?scp=33751022590&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33751022590&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33751022590

SN - 142440035X

SN - 9781424400355

T3 - 2006 IEEE Information Theory Workshop, ITW 2006

SP - 332

EP - 336

BT - 2006 IEEE Information Theory Workshop, ITW 2006

Y2 - 13 March 2006 through 17 March 2006

ER -